In hydrocarbon exploration, accurately understanding the economic projections of a reservoir is vitally important. Conventional approaches to such analysis include the use of earth modeling systems that utilize seismic data to simulate subsurface geological structures, such as faults or other stratigraphic features. Seismic-data traces are the record of the reflection of sonic waves from underground. These traces can be denoted as A(x, y, t), the reflection amplitude of time t at surface location (x, y). Seismic interpretation results include volumes and horizons, which are ultimately utilized to generate a model of the reservoir representative of the structure (stratigraphic layers, faults, etc.) of the formation.
The seismic volumes are three-dimensional volume datasets within a 3D seismic survey or two-dimensional datasets along 2D seismic line. Horizons that are interpreted from the seismic volumes represent the stratigraphic layers along the reservoir model. During the interpretation workflow, additional seismic attributes volumes are generated from the parent input volumes to represent some measured or calculated seismic-petrophysical reservoir property. Likewise, additional horizon attributes are extracted from a parent input horizon and parent input volume to gain better understand of reservoir stratigraphic features. Information from different volume and horizon datasets are extracted to thereby analyze the desired sub-surface geological structures.
The extracted information may be arranged in an inventory tree fashion. Managing the voluminous datasets in the inventory tree, however, has been a longstanding challenge in the industry. As the user creates more and more datasets, the inventory tree eventually becomes unmanageable because conventional models fail to display the relationship between the datasets or their processing history. As a result, the inventory tree ultimately becomes an unintelligible listing of data, with no real method by which to decipher their interrelationships.
In view of the foregoing, there is a need in the art for a system to intelligently group seismic interpretation data in an inventory tree, thus enabling users to analyze numerous volume and horizon datasets in an effective manner to thereby accurately determine the economic projections of a reservoir.